Artificial neural networks are computing systems with an architecture based on biological neural networks. Artificial neural networks can be trained, using training data, to learn about how to perform a certain computing task.
A neural network may include a set of processing nodes. As part of a neural network computation, each processing node can process a piece of the input data based on a weight to generate an intermediate output. The intermediate outputs can be processed by an activation function to generate activation outputs, which can be further processed to generate a decision or an output. A mapping table can be used to approximation activation function processing, in which a plurality of inputs can be mapped to a plurality of candidate outputs, with each candidate output representing a result of performing activation function processing on an input. The activation function may be parametric to adapt the decision making of a neural network for different applications, which can improve operation flexibility. However, using a mapping table to support parametric activation function processing may lead to vastly expanding the mapping table to map the plurality of inputs to multiple sets of candidate outputs for different parametric variants of the parametric activation function, which increases not only the hardware resources required to store the mapping table but also the latency in accessing the mapping table.